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eWEEK IT Science Case Study: How to Reduce a Big AWS Bill

IT SCIENCE CASE STUDY: MetroStar Systems, a leading provider of IT services and consulting primarily to government agencies, was under executive direction to reduce its Amazon Web Services bill. When it discovered Turbonomic, its problems were solved.

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Here is the latest article in an eWEEK feature series called IT Science, in which we look at what actually happens at the intersection of new-gen IT and legacy systems.

Unless it’s brand new and right off various assembly lines, servers, storage and networking inside every IT system can be considered “legacy.” This is because the iteration of both hardware and software products is speeding up all the time. It’s not unusual for an app-maker, for example, to update and/or patch for security purposes an application a few times a month, or even a week. Some apps are updated daily! Hardware moves a little slower, but manufacturing cycles are also speeding up.

These articles describe new-gen industry solutions. The idea is to look at real-world examples of how new-gen IT products and services are making a difference in production each day. Most of them are success stories, but there will also be others about projects that blew up. We’ll have IT integrators, system consultants, analysts and other experts helping us with these as needed.

Further reading

Name the problem to be solved: The operations team at MetroStar Systems, a leading provider of IT services and consulting primarily to government agencies, was under executive direction to reduce its Amazon Web Services bill. As the company expanded its practice areas into mobile development and cyber-security, its developers required a wider range of environments in which to do their projects, including on-premises virtual environments and hybrid-cloud environments. This created greater management complexity for MetroStar’s operations group.

To support this expansion, the company decided to move to AWS rather than buying new hardware to provide the flexibility the organization needed. The problem, however, was that its hybrid-cloud environment was incorrectly overprovisioned, resulting in the organization ringing up cloud bills of more than twice what it expected in its first month.

The solution: MetroStar turned to Turbonomic to optimize its AWS environment automatically. Once Turbonomic was deployed within its AWS environment, the AI-powered decision engine immediately identified several instances of overspend and detected several savings opportunities for the company by unlocking AWS Reserved Instances. Through the utilization of Turbonomic, MetroStar was able to reduce its monthly cloud bill by 25 percent and expects to save an additional 30 percent going forward.

Describe the strategy that went into finding the solution: The MetroStar operations team had already suspected that many of its current instances were not on the proper storage tier, because many provisioning decisions were made by non-infrastructure personnel. The team initially used AWS Trusted Advisor to provide cost suggestions but quickly found the tool to be too complex and time-consuming. Logging in to every instance and spending hours on discovery was not feasible, and the company soon began pursuing other options to manage the environment and reduce spending.

MetroStar needed a way to reduce its cloud bills without demanding an unreasonable amount of the IT team’s time. The logical next step was to look for a solution it could trust to automate its AWS workload sizing and placement decisions and reduce spend.

The latest iteration of Turbonomic’s flagship product continuously automates AWS Reserved Instance utilization in conjunction with resizing actions on storage and database services, unlocking additional savings. This was critical for MetroStar, who was running several workloads continuously that should have been making use of Reserved Instances.

Describe how the deployment went, how long it took, and if it came off as planned: Within hours of deployment, the Turbonomic platform identified several instances of cloud overspend.

Describe the result, new efficiencies gained and what was learned from the project: After turning to Turbonomic, the MetroStar team got the results it sought—instantaneously. By automating Turbonomic AWS storage-tier optimization recommendations, the team rapidly saw a 25 percent reduction in its monthly spend.

In addition to the real dollars saved, thanks to Turbonomic, MetroStar continues to see improvements around performance and policy compliance as the platform continues to automate the environment in the background. The team has begun to take cloud instance sizing recommendations and expects to save an additional 30 percent on its AWS bill in the coming months.

Key learnings: Soon after implementing Turbonomic for its AWS environment, the operations team was given ownership of a dozen customer environments that needed to be migrated. Turbonomic enabled the team to quickly analyze potential migrations between these estates and AWS, and execute these migrations without impacting performance and while abiding by business compliance policies.

Turbonomic migration planning reduces time and uncertainty and eliminates risk from the equation by recommending the optimal AWS resources on which to run. With Turbonomic, the operations team assumed responsibility for these environments without growing the team.

Chris J. Preimesberger

Chris J. Preimesberger is Editor-in-Chief of eWEEK and responsible for all the publication's coverage. In his 13 years and more than 4,000 articles at eWEEK, he has distinguished himself in reporting...

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